Addressing Grand Challenges in Organization Theory-System Change through Theory, Engagement & Action
Bibliographic record
Abstract
The Covid-19 pandemic warrants increased scholarly attention to grand challenges, such as inequality, poverty, climate change, ecological imbalances, socioeconomic and political crises along with their extended impacts (Davis, 2020; Ferraro, Etzio, & Gehman, 2015; George et al., 2016; Munir, 2020; Pio & Waddock, 2020). Whereas mainstream organizational theorizing brings those challenges in as part of our empirical contexts, or in our managerial implications, this symposium will discuss how we can address head-on the importance of grand challenges, make them a core aspect of our research, and accordingly develop theoretical and empirical approaches that may be seen as a ‘grand challenges turn’ in our field. Some recent examples include work on the patriarchy of microfinance (Zhao & Wry, 2016) and providing toilets in Indian villages (Mair, Wolf, & Seelos, 2016). Nevertheless, even scholars working on grand challenges tend to focus on a particular problem and may lose sight of the broader systemic challenges of these issues (Munir, 2019).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".